In eukaryotic cells, DNA is packaged into chromatin, a dynamic structure that can shift between more open (euchromatin) and condensed (heterochromatin) states to regulate processes like gene expression, DNA repair, and genome organization. This regulation is controlled by chromatin regulators, i.e. proteins that add, remove, or interpret epigenetic modifications, as well as remodel chromatin structure, working alongside transcription factors. These mechanisms are highly conserved across diverse eukaryotic species, underscoring their fundamental biological importance. However, experimentally testing the full function of these proteins remains challenging. Current high-throughput approaches often rely on protein fragments rather than full-length chromatin regulators, which can miss key functional domains and enzymatic activities. Additionally, most chromatin engineering has been developed in a few model systems, creating a need for more versatile tools that can function across a broader range of organisms, including plants and other less-studied eukaryotes. This invention comprises a chromatin regulator protein fused to a DNA-binding protein that in turn modifies gene transcription. The inventors used a multi-kingdom, full length chromatin regulator (CR) library to uncover several potent chromatin regulator proteins. These proteins include the human proteins SAP25, MBD3, RCOR1, MTA2, WDR82, DPY30, the plant proteins CMT3, SWC2, or the yeast proteins CHZ1, IES5, and TTI1 respectively. These proteins are then fused to DNA binding proteins with the product of that fusion being referred to as CR fusion proteins. These CR fusion proteins are then able to proteins to increase or decrease transcription of specific genes in eukaryotic cells when introduced to cells with specific nucleic acids.

Over the past decade, single-cell sequencing has become widely used in biology and medicine. To analyze many cells at once, each cell must be labeled with a unique DNA barcode so its molecules can be identified after sequencing. This is usually done by capturing cells on barcode beads, where each bead carries a different DNA sequence. However, because these barcodes are randomly generated and randomly paired with cells, researchers cannot know which barcode belongs to which cell before sequencing, making it difficult to link sequencing data with other measurements from the same cell. This creates a clear gap in the market for barcoding approaches that provide predefined, traceable cell identities prior to sequencing. This invention, Optically Recognizable Barcoded Beads (ORBBs), comprises a method in which each barcode bead is optically unique such that the DNA sequence for a given bead can be determined by imaging the bead with a fluorescent microscope. The key idea behind ORBBs is that each bead has geometrically distinct regions that can be fluorescently labeled within a single bead. This drastically increases the number of unique fluorescent barcodes that can be produced by these beads. ORBBs are able to be barcoded through the same standard split pool process that other commercially available barcoded beads use with one key modification - each well the barcode beads pass through has a unique combination of oligo conjugated fluorophores, creating a unique barcode on each bead.

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Machine learning has transitioned from traditional supervised learning to more resource-efficient sketching and federated techniques. Early compressive learning relied on hand-crafted random projections and task-specific iterative solvers. While these methods reduced data volume, they were inflexible because a change in data distribution or task required a complete redesign of the projection. Concurrently, privacy-preserving needs led to the rise of federated learning and differential privacy. However, these methods often struggled with high communication costs and the inability to merge model updates effectively across different architectures. Until recently, the state of the art remained fairly bifurcated, where one could have either high-accuracy iterative training on raw data, or efficient but brittle and task-specific compressed representations that lacked generalizability across diverse analytical tasks, e.g., Principal Component Analysis (PCA), regression, clustering.

Researchers at the University of California, Davis have developed a method for creating annealed microgel scaffolds using polyethylene glycol-vinyl sulfone, offering improved efficiency and shelf life.

Researchers at the University of California, Davis have developed a method and composition that direct the growth of long, coronally oriented blood vessels in tissue grafts to improve vascularization and clinical transplant outcomes.

Researchers at the University of California, Davis have developed a unique non-sacrificial synthetic peptide substrate designed to inhibit plasmin activity and prevent tumor progression and ascites formation in cancers characterized by elevated plasmin levels.

Researchers at the University of California, Davis have developed engineered amyloid fibrils composed of modified β-solenoid proteins fused with pathogen-binding domains that provide ultra-sensitive, stable, and versatile platforms for detecting viruses and other pathogens.